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This is an exciting era for exo-planetary exploration. The recently launched JWST, and other upcoming space missions such as Ariel, Twinkle and ELTs are set to bring fresh insights to the convoluted processes of planetary formation and evolution and its connections to atmospheric compositions. However, with new opportunities come new challenges. The field of exoplanet atmospheres is already struggling with the incoming volume and quality of data, and machine learning (ML) techniques lands itself as a promising alternative. Developing techniques of this kind is an inter-disciplinary task, one that requires domain knowledge of the field, access to relevant tools and expert insights on the capability and limitations of current ML models. These stringent requirements have so far limited the developments of ML in the field to a few isolated initiatives. In this paper, We present the Atmospheric Big Challenge Database (ABC Database), a carefully designed, organised and publicly available database dedicated to the study of the inverse problem in the context of exoplanetary studies. We have generated 105,887 forward models and 26,109 complementary posterior distributions generated with Nested Sampling algorithm. Alongside with the database, this paper provides a jargon-free introduction to non-field experts interested to dive into the intricacy of atmospheric studies. This database forms the basis for a multitude of research directions, including, but not limited to, developing rapid inference techniques, benchmarking model performance and mitigating data drifts. A successful application of this database is demonstrated in the NeurIPS Ariel ML Data Challenge 2022.